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Audio-driven human body motion analysis and synthesis

By: Tekalp, A.M.; Erzin, E.; Yemez, Y.; Bozkurt, E.; Ofli, F.; Tilmanne, J.; Canton-Ferrer, C.; Demir, Y.;

2008 / IEEE / 978-1-4244-1483-3


This item was taken from the IEEE Conference ' Audio-driven human body motion analysis and synthesis ' This paper presents a framework for audio-driven human body motion analysis and synthesis. We address the problem in the context of a dance performance, where gestures and movements of the dancer are mainly driven by a musical piece and characterized by the repetition of a set of dance figures. The system is trained in a supervised manner using the multiview video recordings of the dancer. The human body posture is extracted from multiview video information without any human intervention using a novel marker-based algorithm based on annealing particle filtering. Audio is analyzed to extract beat and tempo information. The joint analysis of audio and motion features provides a correlation model that is then used to animate a dancing avatar when driven with any musical piece of the same genre. Results are provided showing the effectiveness of the proposed algorithm.